Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations5058
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory441.7 B

Variable types

Categorical9
Numeric10

Alerts

CashbackAmount is highly overall correlated with PreferedOrderCatHigh correlation
CouponUsed is highly overall correlated with OrderCountHigh correlation
OrderCount is highly overall correlated with CouponUsedHigh correlation
PreferedOrderCat is highly overall correlated with CashbackAmountHigh correlation
Tenure has 457 (9.0%) zeros Zeros
CouponUsed has 912 (18.0%) zeros Zeros
DaySinceLastOrder has 427 (8.4%) zeros Zeros

Reproduction

Analysis started2025-06-19 19:26:00.793204
Analysis finished2025-06-19 19:26:20.868414
Duration20.08 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size326.0 KiB
0
4220 
1
838 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5058
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 4220
83.4%
1 838
 
16.6%

Length

2025-06-19T19:26:20.993495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:26:21.070603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4220
83.4%
1 838
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 4220
83.4%
1 838
 
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4220
83.4%
1 838
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4220
83.4%
1 838
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4220
83.4%
1 838
 
16.6%

Tenure
Real number (ℝ)

Zeros 

Distinct36
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142151
Minimum0
Maximum61
Zeros457
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-06-19T19:26:21.165448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q315
95-th percentile27
Maximum61
Range61
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.3962664
Coefficient of variation (CV)0.82785854
Kurtosis0.21026547
Mean10.142151
Median Absolute Deviation (MAD)6
Skewness0.78873155
Sum51299
Variance70.49729
MonotonicityNot monotonic
2025-06-19T19:26:21.317533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 618
 
12.2%
9 458
 
9.1%
0 457
 
9.0%
8 233
 
4.6%
10 196
 
3.9%
7 195
 
3.9%
5 189
 
3.7%
4 185
 
3.7%
3 176
 
3.5%
11 174
 
3.4%
Other values (26) 2177
43.0%
ValueCountFrequency (%)
0 457
9.0%
1 618
12.2%
2 152
 
3.0%
3 176
 
3.5%
4 185
 
3.7%
5 189
 
3.7%
6 165
 
3.3%
7 195
 
3.9%
8 233
 
4.6%
9 458
9.1%
ValueCountFrequency (%)
61 1
 
< 0.1%
60 1
 
< 0.1%
51 1
 
< 0.1%
50 1
 
< 0.1%
31 46
0.9%
30 63
1.2%
29 52
1.0%
28 61
1.2%
27 59
1.2%
26 55
1.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size350.0 KiB
Phone
3608 
Computer
1450 

Length

Max length8
Median length5
Mean length5.8600237
Min length5

Characters and Unicode

Total characters29640
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhone
2nd rowPhone
3rd rowPhone
4th rowPhone
5th rowPhone

Common Values

ValueCountFrequency (%)
Phone 3608
71.3%
Computer 1450
28.7%

Length

2025-06-19T19:26:21.456331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:26:21.552119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
phone 3608
71.3%
computer 1450
28.7%

Most occurring characters

ValueCountFrequency (%)
o 5058
17.1%
e 5058
17.1%
P 3608
12.2%
h 3608
12.2%
n 3608
12.2%
C 1450
 
4.9%
m 1450
 
4.9%
p 1450
 
4.9%
u 1450
 
4.9%
t 1450
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5058
17.1%
e 5058
17.1%
P 3608
12.2%
h 3608
12.2%
n 3608
12.2%
C 1450
 
4.9%
m 1450
 
4.9%
p 1450
 
4.9%
u 1450
 
4.9%
t 1450
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5058
17.1%
e 5058
17.1%
P 3608
12.2%
h 3608
12.2%
n 3608
12.2%
C 1450
 
4.9%
m 1450
 
4.9%
p 1450
 
4.9%
u 1450
 
4.9%
t 1450
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5058
17.1%
e 5058
17.1%
P 3608
12.2%
h 3608
12.2%
n 3608
12.2%
C 1450
 
4.9%
m 1450
 
4.9%
p 1450
 
4.9%
u 1450
 
4.9%
t 1450
 
4.9%

CityTier
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size326.0 KiB
1
3294 
3
1569 
2
 
195

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5058
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 3294
65.1%
3 1569
31.0%
2 195
 
3.9%

Length

2025-06-19T19:26:21.648163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:26:21.732258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 3294
65.1%
3 1569
31.0%
2 195
 
3.9%

Most occurring characters

ValueCountFrequency (%)
1 3294
65.1%
3 1569
31.0%
2 195
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3294
65.1%
3 1569
31.0%
2 195
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3294
65.1%
3 1569
31.0%
2 195
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3294
65.1%
3 1569
31.0%
2 195
 
3.9%

WarehouseToHome
Real number (ℝ)

Distinct46
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.480308
Minimum5
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-06-19T19:26:21.844028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median14
Q320
95-th percentile32
Maximum35
Range30
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.1213135
Coefficient of variation (CV)0.5246222
Kurtosis-0.20608945
Mean15.480308
Median Absolute Deviation (MAD)5
Skewness0.91856859
Sum78299.4
Variance65.955732
MonotonicityNot monotonic
2025-06-19T19:26:22.016541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
9 502
 
9.9%
8 403
 
8.0%
7 376
 
7.4%
14 298
 
5.9%
16 288
 
5.7%
6 283
 
5.6%
15 260
 
5.1%
11 257
 
5.1%
10 239
 
4.7%
13 220
 
4.3%
Other values (36) 1932
38.2%
ValueCountFrequency (%)
5 8
 
0.2%
6 283
5.6%
7 376
7.4%
8 403
8.0%
9 502
9.9%
9.8 2
 
< 0.1%
10 239
4.7%
11 257
5.1%
11.2 3
 
0.1%
11.8 1
 
< 0.1%
ValueCountFrequency (%)
35 132
2.6%
34 57
1.1%
33 60
1.2%
32 87
1.7%
31 90
1.8%
30 84
1.7%
29 71
1.4%
28 59
1.2%
27 70
1.4%
26 76
1.5%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size365.4 KiB
Debit Card
2090 
Credit Card
1593 
E wallet
562 
COD
455 
UPI
358 

Length

Max length11
Median length10
Mean length8.9675761
Min length3

Characters and Unicode

Total characters45358
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDebit Card
2nd rowUPI
3rd rowDebit Card
4th rowDebit Card
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Debit Card 2090
41.3%
Credit Card 1593
31.5%
E wallet 562
 
11.1%
COD 455
 
9.0%
UPI 358
 
7.1%

Length

2025-06-19T19:26:22.166153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:26:22.271414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
card 3683
39.6%
debit 2090
22.5%
credit 1593
17.1%
e 562
 
6.0%
wallet 562
 
6.0%
cod 455
 
4.9%
upi 358
 
3.8%

Most occurring characters

ValueCountFrequency (%)
C 5731
12.6%
d 5276
11.6%
r 5276
11.6%
4245
9.4%
e 4245
9.4%
t 4245
9.4%
a 4245
9.4%
i 3683
8.1%
D 2545
5.6%
b 2090
 
4.6%
Other values (7) 3777
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45358
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 5731
12.6%
d 5276
11.6%
r 5276
11.6%
4245
9.4%
e 4245
9.4%
t 4245
9.4%
a 4245
9.4%
i 3683
8.1%
D 2545
5.6%
b 2090
 
4.6%
Other values (7) 3777
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45358
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 5731
12.6%
d 5276
11.6%
r 5276
11.6%
4245
9.4%
e 4245
9.4%
t 4245
9.4%
a 4245
9.4%
i 3683
8.1%
D 2545
5.6%
b 2090
 
4.6%
Other values (7) 3777
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45358
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 5731
12.6%
d 5276
11.6%
r 5276
11.6%
4245
9.4%
e 4245
9.4%
t 4245
9.4%
a 4245
9.4%
i 3683
8.1%
D 2545
5.6%
b 2090
 
4.6%
Other values (7) 3777
8.3%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size344.8 KiB
Male
3040 
Female
2018 

Length

Max length6
Median length4
Mean length4.7979439
Min length4

Characters and Unicode

Total characters24268
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 3040
60.1%
Female 2018
39.9%

Length

2025-06-19T19:26:22.400320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:26:22.490910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 3040
60.1%
female 2018
39.9%

Most occurring characters

ValueCountFrequency (%)
e 7076
29.2%
a 5058
20.8%
l 5058
20.8%
M 3040
12.5%
F 2018
 
8.3%
m 2018
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7076
29.2%
a 5058
20.8%
l 5058
20.8%
M 3040
12.5%
F 2018
 
8.3%
m 2018
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7076
29.2%
a 5058
20.8%
l 5058
20.8%
M 3040
12.5%
F 2018
 
8.3%
m 2018
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7076
29.2%
a 5058
20.8%
l 5058
20.8%
M 3040
12.5%
F 2018
 
8.3%
m 2018
 
8.3%

HourSpendOnApp
Real number (ℝ)

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9185844
Minimum0
Maximum5
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-06-19T19:26:22.563926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.71336753
Coefficient of variation (CV)0.24442244
Kurtosis-0.61200425
Mean2.9185844
Median Absolute Deviation (MAD)0.4
Skewness0.00049044344
Sum14762.2
Variance0.50889323
MonotonicityNot monotonic
2025-06-19T19:26:22.668554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 2431
48.1%
2 1341
26.5%
4 1051
20.8%
2.6 52
 
1.0%
2.8 46
 
0.9%
1 33
 
0.7%
2.4 32
 
0.6%
2.2 24
 
0.5%
3.2 18
 
0.4%
3.4 12
 
0.2%
Other values (3) 18
 
0.4%
ValueCountFrequency (%)
0 3
 
0.1%
1 33
 
0.7%
2 1341
26.5%
2.2 24
 
0.5%
2.4 32
 
0.6%
2.6 52
 
1.0%
2.8 46
 
0.9%
3 2431
48.1%
3.2 18
 
0.4%
3.4 12
 
0.2%
ValueCountFrequency (%)
5 3
 
0.1%
4 1051
20.8%
3.6 12
 
0.2%
3.4 12
 
0.2%
3.2 18
 
0.4%
3 2431
48.1%
2.8 46
 
0.9%
2.6 52
 
1.0%
2.4 32
 
0.6%
2.2 24
 
0.5%

NumberOfDeviceRegistered
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6862396
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-06-19T19:26:22.766274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0302007
Coefficient of variation (CV)0.27947198
Kurtosis0.55932671
Mean3.6862396
Median Absolute Deviation (MAD)1
Skewness-0.40326877
Sum18645
Variance1.0613135
MonotonicityNot monotonic
2025-06-19T19:26:22.856437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 2126
42.0%
3 1519
30.0%
5 797
 
15.8%
2 253
 
5.0%
1 217
 
4.3%
6 146
 
2.9%
ValueCountFrequency (%)
1 217
 
4.3%
2 253
 
5.0%
3 1519
30.0%
4 2126
42.0%
5 797
 
15.8%
6 146
 
2.9%
ValueCountFrequency (%)
6 146
 
2.9%
5 797
 
15.8%
4 2126
42.0%
3 1519
30.0%
2 253
 
5.0%
1 217
 
4.3%

PreferedOrderCat
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size371.6 KiB
Phone
1849 
Laptop & Accessory
1844 
Fashion
760 
Grocery
364 
Others
241 

Length

Max length18
Median length7
Mean length10.231514
Min length5

Characters and Unicode

Total characters51751
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaptop & Accessory
2nd rowPhone
3rd rowPhone
4th rowLaptop & Accessory
5th rowPhone

Common Values

ValueCountFrequency (%)
Phone 1849
36.6%
Laptop & Accessory 1844
36.5%
Fashion 760
15.0%
Grocery 364
 
7.2%
Others 241
 
4.8%

Length

2025-06-19T19:26:22.981060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:26:23.093996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
phone 1849
21.1%
laptop 1844
21.1%
1844
21.1%
accessory 1844
21.1%
fashion 760
8.7%
grocery 364
 
4.2%
others 241
 
2.8%

Most occurring characters

ValueCountFrequency (%)
o 6661
12.9%
s 4689
 
9.1%
e 4298
 
8.3%
c 4052
 
7.8%
3688
 
7.1%
p 3688
 
7.1%
h 2850
 
5.5%
r 2813
 
5.4%
n 2609
 
5.0%
a 2604
 
5.0%
Other values (10) 13799
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51751
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 6661
12.9%
s 4689
 
9.1%
e 4298
 
8.3%
c 4052
 
7.8%
3688
 
7.1%
p 3688
 
7.1%
h 2850
 
5.5%
r 2813
 
5.4%
n 2609
 
5.0%
a 2604
 
5.0%
Other values (10) 13799
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51751
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 6661
12.9%
s 4689
 
9.1%
e 4298
 
8.3%
c 4052
 
7.8%
3688
 
7.1%
p 3688
 
7.1%
h 2850
 
5.5%
r 2813
 
5.4%
n 2609
 
5.0%
a 2604
 
5.0%
Other values (10) 13799
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51751
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 6661
12.9%
s 4689
 
9.1%
e 4298
 
8.3%
c 4052
 
7.8%
3688
 
7.1%
p 3688
 
7.1%
h 2850
 
5.5%
r 2813
 
5.4%
n 2609
 
5.0%
a 2604
 
5.0%
Other values (10) 13799
26.7%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size326.0 KiB
3
1499 
1
1074 
5
973 
4
926 
2
586 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5058
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 1499
29.6%
1 1074
21.2%
5 973
19.2%
4 926
18.3%
2 586
 
11.6%

Length

2025-06-19T19:26:23.219861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:26:23.318749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1499
29.6%
1 1074
21.2%
5 973
19.2%
4 926
18.3%
2 586
 
11.6%

Most occurring characters

ValueCountFrequency (%)
3 1499
29.6%
1 1074
21.2%
5 973
19.2%
4 926
18.3%
2 586
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1499
29.6%
1 1074
21.2%
5 973
19.2%
4 926
18.3%
2 586
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1499
29.6%
1 1074
21.2%
5 973
19.2%
4 926
18.3%
2 586
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1499
29.6%
1 1074
21.2%
5 973
19.2%
4 926
18.3%
2 586
 
11.6%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
Married
2663 
Single
1547 
Divorced
848 

Length

Max length8
Median length7
Mean length6.8618031
Min length6

Characters and Unicode

Total characters34707
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 2663
52.6%
Single 1547
30.6%
Divorced 848
 
16.8%

Length

2025-06-19T19:26:23.454358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:26:23.547675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married 2663
52.6%
single 1547
30.6%
divorced 848
 
16.8%

Most occurring characters

ValueCountFrequency (%)
r 6174
17.8%
i 5058
14.6%
e 5058
14.6%
d 3511
10.1%
a 2663
7.7%
M 2663
7.7%
S 1547
 
4.5%
n 1547
 
4.5%
g 1547
 
4.5%
l 1547
 
4.5%
Other values (4) 3392
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34707
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 6174
17.8%
i 5058
14.6%
e 5058
14.6%
d 3511
10.1%
a 2663
7.7%
M 2663
7.7%
S 1547
 
4.5%
n 1547
 
4.5%
g 1547
 
4.5%
l 1547
 
4.5%
Other values (4) 3392
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34707
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 6174
17.8%
i 5058
14.6%
e 5058
14.6%
d 3511
10.1%
a 2663
7.7%
M 2663
7.7%
S 1547
 
4.5%
n 1547
 
4.5%
g 1547
 
4.5%
l 1547
 
4.5%
Other values (4) 3392
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34707
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 6174
17.8%
i 5058
14.6%
e 5058
14.6%
d 3511
10.1%
a 2663
7.7%
M 2663
7.7%
S 1547
 
4.5%
n 1547
 
4.5%
g 1547
 
4.5%
l 1547
 
4.5%
Other values (4) 3392
9.8%

NumberOfAddress
Real number (ℝ)

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1896006
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-06-19T19:26:23.634627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile10
Maximum22
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5743127
Coefficient of variation (CV)0.614453
Kurtosis1.1133353
Mean4.1896006
Median Absolute Deviation (MAD)1
Skewness1.1106097
Sum21191
Variance6.6270858
MonotonicityNot monotonic
2025-06-19T19:26:23.736881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 1243
24.6%
3 1146
22.7%
4 522
10.3%
5 513
10.1%
6 344
 
6.8%
1 341
 
6.7%
8 248
 
4.9%
7 233
 
4.6%
9 210
 
4.2%
10 172
 
3.4%
Other values (5) 86
 
1.7%
ValueCountFrequency (%)
1 341
 
6.7%
2 1243
24.6%
3 1146
22.7%
4 522
10.3%
5 513
10.1%
6 344
 
6.8%
7 233
 
4.6%
8 248
 
4.9%
9 210
 
4.2%
10 172
 
3.4%
ValueCountFrequency (%)
22 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
11 82
 
1.6%
10 172
3.4%
9 210
4.2%
8 248
4.9%
7 233
4.6%
6 344
6.8%

Complain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size326.0 KiB
0
3628 
1
1430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5058
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3628
71.7%
1 1430
 
28.3%

Length

2025-06-19T19:26:23.847096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-19T19:26:23.929645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3628
71.7%
1 1430
 
28.3%

Most occurring characters

ValueCountFrequency (%)
0 3628
71.7%
1 1430
 
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3628
71.7%
1 1430
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3628
71.7%
1 1430
 
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3628
71.7%
1 1430
 
28.3%
Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.672993
Minimum11
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-06-19T19:26:24.004312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q113
median15
Q318
95-th percentile23
Maximum26
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5872451
Coefficient of variation (CV)0.22888066
Kurtosis-0.13022624
Mean15.672993
Median Absolute Deviation (MAD)2
Skewness0.83537364
Sum79274
Variance12.868327
MonotonicityNot monotonic
2025-06-19T19:26:24.126195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
15 727
14.4%
13 677
13.4%
14 667
13.2%
12 657
13.0%
11 345
6.8%
16 297
5.9%
18 290
 
5.7%
19 284
 
5.6%
17 262
 
5.2%
20 219
 
4.3%
Other values (6) 633
12.5%
ValueCountFrequency (%)
11 345
6.8%
12 657
13.0%
13 677
13.4%
14 667
13.2%
15 727
14.4%
16 297
5.9%
17 262
 
5.2%
18 290
 
5.7%
19 284
 
5.6%
20 219
 
4.3%
ValueCountFrequency (%)
26 28
 
0.6%
25 66
 
1.3%
24 76
 
1.5%
23 130
2.6%
22 164
3.2%
21 169
3.3%
20 219
4.3%
19 284
5.6%
18 290
5.7%
17 262
5.2%

CouponUsed
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6640965
Minimum0
Maximum6
Zeros912
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-06-19T19:26:24.233222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5358288
Coefficient of variation (CV)0.92292051
Kurtosis1.6545525
Mean1.6640965
Median Absolute Deviation (MAD)1
Skewness1.4403744
Sum8417
Variance2.35877
MonotonicityNot monotonic
2025-06-19T19:26:24.333746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 2080
41.1%
2 1158
22.9%
0 912
18.0%
3 310
 
6.1%
6 287
 
5.7%
4 186
 
3.7%
5 125
 
2.5%
ValueCountFrequency (%)
0 912
18.0%
1 2080
41.1%
2 1158
22.9%
3 310
 
6.1%
4 186
 
3.7%
5 125
 
2.5%
6 287
 
5.7%
ValueCountFrequency (%)
6 287
 
5.7%
5 125
 
2.5%
4 186
 
3.7%
3 310
 
6.1%
2 1158
22.9%
1 2080
41.1%
0 912
18.0%

OrderCount
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8525109
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-06-19T19:26:24.430296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3618639
Coefficient of variation (CV)0.82799469
Kurtosis1.0866285
Mean2.8525109
Median Absolute Deviation (MAD)1
Skewness1.5295723
Sum14428
Variance5.5784009
MonotonicityNot monotonic
2025-06-19T19:26:24.526753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 2028
40.1%
1 1528
30.2%
3 351
 
6.9%
9 305
 
6.0%
7 195
 
3.9%
4 187
 
3.7%
5 175
 
3.5%
8 162
 
3.2%
6 127
 
2.5%
ValueCountFrequency (%)
1 1528
30.2%
2 2028
40.1%
3 351
 
6.9%
4 187
 
3.7%
5 175
 
3.5%
6 127
 
2.5%
7 195
 
3.9%
8 162
 
3.2%
9 305
 
6.0%
ValueCountFrequency (%)
9 305
 
6.0%
8 162
 
3.2%
7 195
 
3.9%
6 127
 
2.5%
5 175
 
3.5%
4 187
 
3.7%
3 351
 
6.9%
2 2028
40.1%
1 1528
30.2%

DaySinceLastOrder
Real number (ℝ)

Zeros 

Distinct34
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4113484
Minimum0
Maximum11
Zeros427
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-06-19T19:26:24.644839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2278582
Coefficient of variation (CV)0.7317169
Kurtosis-0.90784849
Mean4.4113484
Median Absolute Deviation (MAD)2
Skewness0.52077357
Sum22312.6
Variance10.419068
MonotonicityNot monotonic
2025-06-19T19:26:24.794083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3 843
16.7%
2 727
14.4%
1 540
10.7%
8 495
9.8%
0 427
8.4%
7 416
8.2%
4 398
7.9%
11 281
 
5.6%
9 263
 
5.2%
5 201
 
4.0%
Other values (24) 467
9.2%
ValueCountFrequency (%)
0 427
8.4%
0.8 10
 
0.2%
1 540
10.7%
1.2 8
 
0.2%
1.6 4
 
0.1%
1.8 11
 
0.2%
2 727
14.4%
2.2 4
 
0.1%
2.4 1
 
< 0.1%
2.6 12
 
0.2%
ValueCountFrequency (%)
11 281
5.6%
10 137
 
2.7%
9 263
5.2%
8 495
9.8%
7 416
8.2%
6.6 6
 
0.1%
6.4 13
 
0.3%
6 106
 
2.1%
5.8 17
 
0.3%
5.6 2
 
< 0.1%

CashbackAmount
Real number (ℝ)

High correlation 

Distinct2529
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.80357
Minimum116.01
Maximum318.7431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.0 KiB
2025-06-19T19:26:24.951394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum116.01
5-th percentile123.07
Q1145.9025
median163.855
Q3197.64
95-th percentile292.526
Maximum318.7431
Range202.7331
Interquartile range (IQR)51.7375

Descriptive statistics

Standard deviation48.74129
Coefficient of variation (CV)0.27412999
Kurtosis0.82964656
Mean177.80357
Median Absolute Deviation (MAD)23.455
Skewness1.2227073
Sum899330.45
Variance2375.7134
MonotonicityNot monotonic
2025-06-19T19:26:25.124287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.01 53
 
1.0%
318.7431 53
 
1.0%
123.42 8
 
0.2%
148.42 8
 
0.2%
149.36 8
 
0.2%
188.47 7
 
0.1%
149.5 6
 
0.1%
161.42 6
 
0.1%
154.73 6
 
0.1%
146.27 6
 
0.1%
Other values (2519) 4897
96.8%
ValueCountFrequency (%)
116.01 53
1.0%
116.11 2
 
< 0.1%
116.75 2
 
< 0.1%
117.02 2
 
< 0.1%
117.06 2
 
< 0.1%
117.9 1
 
< 0.1%
118.59 2
 
< 0.1%
118.78 1
 
< 0.1%
119.04 2
 
< 0.1%
119.51 1
 
< 0.1%
ValueCountFrequency (%)
318.7431 53
1.0%
318.31 2
 
< 0.1%
318.28 2
 
< 0.1%
317.72 2
 
< 0.1%
317.46 1
 
< 0.1%
317.37 2
 
< 0.1%
317.32 2
 
< 0.1%
317.02 2
 
< 0.1%
316.97 1
 
< 0.1%
316.9 2
 
< 0.1%

Interactions

2025-06-19T19:26:19.029688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:03.079149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:04.382219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:05.732315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:07.010583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:08.288539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:09.526762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:14.892011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:16.386614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:17.756406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:19.166340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:03.205534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:04.501797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:05.855991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:07.129024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:08.418413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:09.648938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:15.113288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:16.525881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:17.894096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:19.295560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:03.337972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:04.630038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:05.988010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:07.260233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:08.542630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:09.797789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:15.302557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:16.648119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:18.011179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:19.433411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:03.462914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:04.771932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:06.113687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:07.395811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:08.661196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:09.933021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:15.428691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:16.801404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:18.125321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:19.563271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:03.586180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:04.907713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:06.252185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:07.509164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:08.777009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:10.059653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:15.562290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:16.922688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:18.255976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:19.701748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:03.712382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:05.031705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:06.366930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:07.636848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:08.887400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:10.177231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:15.701643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:17.064552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:18.378424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:19.848998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:03.841169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:05.184980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:06.501999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:07.777002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:09.018195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:10.321707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:15.840455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:17.220147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:18.517089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:19.998652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:03.961508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:05.337041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:06.622411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:07.900631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:09.140199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:10.474153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:15.971438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:17.350655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:18.638329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-19T19:26:04.113923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:05.469656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-19T19:26:08.027356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:09.273260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:10.620829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:16.112220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:17.484912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:18.759772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:20.279594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:04.246391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:05.601649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:06.888309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:08.149205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:09.401072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:10.759261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:16.247933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:17.605420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-19T19:26:18.893816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-19T19:26:25.346396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CashbackAmountChurnCityTierComplainCouponUsedDaySinceLastOrderGenderHourSpendOnAppMaritalStatusNumberOfAddressNumberOfDeviceRegisteredOrderAmountHikeFromlastYearOrderCountPreferedOrderCatPreferredLoginDevicePreferredPaymentModeSatisfactionScoreTenureWarehouseToHome
CashbackAmount1.0000.1730.1520.0270.2820.3590.0520.2080.0690.2640.2090.0400.3700.7280.0630.0800.0000.4110.004
Churn0.1731.0000.0960.2460.0000.1890.0230.0370.1810.0870.1260.0750.0510.2110.0490.0980.1020.3250.089
CityTier0.1520.0961.0000.0140.0330.0340.0500.0180.0440.0280.0090.0360.0370.2130.0000.4300.0540.0580.046
Complain0.0270.2460.0141.0000.0070.0290.0360.0000.0000.0430.0000.0420.0000.0150.0000.0180.0470.0610.070
CouponUsed0.2820.0000.0330.0071.0000.2920.0400.3120.0240.0960.2550.0670.6690.1250.0000.0070.0000.0870.013
DaySinceLastOrder0.3590.1890.0340.0290.2921.0000.0040.1150.054-0.0800.0340.0080.4380.2170.0000.0320.0190.1880.019
Gender0.0520.0230.0500.0360.0400.0041.0000.0410.0440.0190.0000.0470.0310.0630.0060.0380.0380.0590.060
HourSpendOnApp0.2080.0370.0180.0000.3120.1150.0411.0000.0220.1860.3470.1300.2840.0880.0310.0000.0170.0070.078
MaritalStatus0.0690.1810.0440.0000.0240.0540.0440.0221.0000.0500.0330.0000.0220.0720.0220.0200.2390.0810.057
NumberOfAddress0.2640.0870.0280.0430.096-0.0800.0190.1860.0501.0000.1270.0480.0630.1010.0490.0460.0330.2790.020
NumberOfDeviceRegistered0.2090.1260.0090.0000.2550.0340.0000.3470.0330.1271.0000.0930.2450.0230.0000.0000.009-0.0250.016
OrderAmountHikeFromlastYear0.0400.0750.0360.0420.0670.0080.0470.1300.0000.0480.0931.0000.0570.2090.0000.0270.0310.0260.039
OrderCount0.3700.0510.0370.0000.6690.4380.0310.2840.0220.0630.2450.0571.0000.1750.0260.0000.0170.1520.019
PreferedOrderCat0.7280.2110.2130.0150.1250.2170.0630.0880.0720.1010.0230.2090.1751.0000.0620.0950.0170.2760.090
PreferredLoginDevice0.0630.0490.0000.0000.0000.0000.0060.0310.0220.0490.0000.0000.0260.0621.0000.0390.0290.0540.061
PreferredPaymentMode0.0800.0980.4300.0180.0070.0320.0380.0000.0200.0460.0000.0270.0000.0950.0391.0000.0420.0400.054
SatisfactionScore0.0000.1020.0540.0470.0000.0190.0380.0170.2390.0330.0090.0310.0170.0170.0290.0421.0000.0360.055
Tenure0.4110.3250.0580.0610.0870.1880.0590.0070.0810.279-0.0250.0260.1520.2760.0540.0400.0361.000-0.047
WarehouseToHome0.0040.0890.0460.0700.0130.0190.0600.0780.0570.0200.0160.0390.0190.0900.0610.0540.055-0.0471.000

Missing values

2025-06-19T19:26:20.510257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-19T19:26:20.731871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ChurnTenurePreferredLoginDeviceCityTierWarehouseToHomePreferredPaymentModeGenderHourSpendOnAppNumberOfDeviceRegisteredPreferedOrderCatSatisfactionScoreMaritalStatusNumberOfAddressComplainOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmount
014.0Phone36.0Debit CardFemale3.03Laptop & Accessory2Single9111.01.01.05.0159.93
119.0Phone18.0UPIMale3.04Phone3Single7115.00.01.00.0120.90
219.0Phone130.0Debit CardMale2.04Phone3Single6114.00.01.03.0120.28
310.0Phone315.0Debit CardMale2.04Laptop & Accessory5Single8023.00.01.03.0134.07
410.0Phone112.0Credit CardMale3.23Phone5Single3011.01.01.03.0129.60
510.0Computer122.0Debit CardFemale3.05Phone5Single2122.04.06.07.0139.19
619.0Phone311.0CODMale2.03Laptop & Accessory2Divorced4014.00.01.00.0120.86
719.0Phone16.0Credit CardMale3.03Phone2Divorced3116.02.02.00.0122.93
8113.0Phone39.0E walletMale2.44Phone3Divorced2114.00.01.02.0126.83
919.0Phone131.0Debit CardMale2.05Phone3Single2012.01.01.01.0122.93
ChurnTenurePreferredLoginDeviceCityTierWarehouseToHomePreferredPaymentModeGenderHourSpendOnAppNumberOfDeviceRegisteredPreferedOrderCatSatisfactionScoreMaritalStatusNumberOfAddressComplainOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmount
5617014.0Phone19.0Credit CardFemale4.04Phone3Married4015.01.02.03.0145.01
561809.0Phone18.0Debit CardFemale4.06Phone1Married3013.02.02.02.0154.77
561903.0Phone120.0UPIMale3.05Laptop & Accessory4Married6114.01.02.04.0165.25
5621114.0Phone335.0E walletMale3.05Fashion5Married6114.03.02.01.0233.54
5622013.0Phone331.0E walletFemale3.05Grocery1Married2012.04.02.07.0245.31
562305.0Computer112.0Credit CardMale4.04Laptop & Accessory5Single2020.02.02.04.8224.36
562401.0Phone312.0UPIFemale2.05Phone3Single2019.02.02.01.0154.66
5626013.0Phone113.0Credit CardMale3.05Fashion5Married6016.01.02.02.6224.91
562701.0Phone111.0Debit CardMale3.02Laptop & Accessory4Married3121.01.02.04.0186.42
562908.0Phone115.0Credit CardMale3.02Laptop & Accessory3Married4013.02.02.03.0169.04